Workflows

Wykorzystanie klasyfikatora binarnego do k... (1)

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Wiele klasyfikatorów (np. SVM) pozwala na klasyfikacjÄ™ jedynie w przypadku gdy zmienna celu jest binarna. Poniższy przepÅ‚yw pokazuje, w jaki sposób operator Polynomial By Binomial Classificaiton pozwala agregować odpowiedzi klasyfikatorów binarnych dla przypadków, w których zmienna celu zawiera wiele różnych wartoÅ›ci.

Created: 2011-05-25 | Last updated: 2011-05-25

Uczenie z macierza kosztow (1)

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Przeplyw pokazuje wykorzystanie operatora MetaCost, umozliwiajacego wprowadzenie macierzy kosztow do procesu uczenia klasyfikatora na przykladzie naiwnego klasyfikatora Bayesa.

Created: 2011-05-25 | Last updated: 2014-03-23

Plotting Training vs Testing Error (Loop +... (1)

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This process increases the parameter C of a support vector machine and hence also the risk for overfitting. It uses an outer loop operator for increasing the parameter value and an inner log operator for storing the current number of applications together with the current errors on the training and the testing data set. At the end of the process, the log data can be plotted (for example with the plotter "Scatter Multiple" with "Count" on the x-axis and both "Training Error" and "Testing Error...

Created: 2011-08-15

Images (1)

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Created: 2011-09-22

Using R to add two columns (1)

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This process shows a simple R script which adds two columns of a data set with R. Of course this is much simpler by using the operator "Generate Attributes" which is done in parallel, but maybe some of you find this short process helpful in order to get started with R.

Created: 2010-12-10 | Last updated: 2010-12-10

Loop Repository and Append Data (1)

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This process flow loops through all exampleset '.ioo' files in a repository directory and appends their values. It can also be used as a template for batch processing on different repository entries, or for general data appending.

Created: 2010-12-10 | Last updated: 2010-12-10

Generate percentages from absolute values (1)

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This process demonstrates how to calculate the percentages of absolute values of one data record. The process first creates an aggregation attribute SumAll and then loops over the absolute value attributes and creates a new relative attributes. Tags: relative, absolute, values, percentage, loop attributes, generate aggregation

Created: 2010-09-14 | Last updated: 2010-09-14

RCOMM Challenge 2: Broken Iris (Preparation) (1)

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This workflow creates the input for the RCOMM 2010 Challenge 2. The solution and description are in workflow "RCOMM Challenge 2: Broken Iris"

Created: 2010-09-17

Automatical Disabling / Enabling of Operat... (1)

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This meta process shows another possibility for automatically optimizing the process layout. The operator "OperatorEnabler" can be used to enable or disable one of its children. Together with one of the parameter optimization operators this can be used to check which operators should be used for optimal results. This is especially useful in order to determine which preprocessing operators should be used for a particular data set - learner combination or if you want to automatically test diffe...

Created: 2010-10-13

Recursive Feature Elimination with fully o... (1)

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The Process shows how you can use the RecursiveFeatureElimination-Operator of the Feature Selection Extension ( https://sourceforge.net/projects/rm-featselext) to select relevant features from a highdimensional dataset.

Created: 2010-10-19 | Last updated: 2010-12-10

Find best feature selection method. (1)

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This processes searches for the best method for feature selection and the best feature subset size. Inside a parameter loop the number of selected features and the feature selection methods are evaluated with a 10fold cross-validation. The Feature Selection Extension ( https://sourceforge.net/projects/rm-featselext ) is required. BUGFIX: Please note that the plugin downloaded before 5th November contains a bug. Due to a bug in RM the TopK-operator sorted the features in the wrong direction. ...

Created: 2010-10-19 | Last updated: 2010-12-10

Concatenate Examples (1)

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This process I'm using for preprocessing is concatenating the values of att2 for all the same values of att1 in att3. ps: Thanks to Matthias for his help.

Created: 2010-10-26 | Last updated: 2010-10-26

10x10fold_XV_with_optimizedSVM_classification (1)

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This process performs 10 round of 10fold cross-validation for classification with a linear SVM. In each traning-phase the parameters C of the linear SVM is tuned by a 5fold cross-validation. The repetions are useful for estimating the real performance on a small sample high variance data set, e.g. microarray data.

Created: 2010-10-28

Is Parameter Optimization Useful (1)

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This process compares two 10x10fold cross-validations with linear SVM for classification. One use the heuristic parameter choice C=1/avg(K(x,x)) and the other uses an internal 5fold cross-validation to tune the parameter C. Afterwards both performance values are compared via ANOVA and T-test to check whether there is a significant difference between both performance values. The behaviour of the optimized 10x10 XV is similar to that of the experiment 10x10fold_XV_with_optimizedSVM_classificat...

Created: 2010-10-28 | Last updated: 2010-10-28

Feature selection with background knowledge (1)

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Sample workflow that exploits background knowledge in the form of links connecting attributes in the different measurement data. The input data is assumed to be labeled and consists of different interconnected biological levels. Additionally a number of files defining links between attributes in the different measurement data are provided. This data was used in the KUP data mining challenge (http://tunedit.org/challenge/ON). Here we focus on two biological levels: protein data measured by ...

Created: 2011-05-25 | Last updated: 2011-05-25

Trim and Replace White Space (1)

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This process removed leading and trailing white spaces from an attribute and replaces inner white spaces of any type by a single space. Might be useful for text processing (if you are not using tokenization anyway...)

Created: 2011-07-07

RBioconductor Tutorial Process (1)

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This Process illustrates how to create complex Data Analysis processes using R Scripts. In this case the Bioconductor Basic Tutorial is splitted into reusable components.

Created: 2011-01-03

JSI Subgroup Discovery operators (1)

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A simple example of how to use subgroup discovery operators for learning subgroup descriptions and how to test their classification accuracy using cross-validation.

Created: 2011-01-10 | Last updated: 2011-01-10

Count co-occurences in matrix with Aggrega... (1)

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The process counts and shows all combinations of values of two attributes and shows them in a count matrix. The process uses the operators Aggregate and Pivot for this purpose.

Created: 2011-01-15

Fast Correlation Based Filter (FCBF) (1)

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Runs FCBF feature selector and accuracy is estimated using Naive Bayes. Grid Search Optimization is used for parameter optimization.

Created: 2011-07-14 | Last updated: 2011-07-14

Fast Correlation Based Filter (FCBF) - Sta... (1)

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Created: 2011-07-14 | Last updated: 2011-07-14

Prediction Analysis of Microarrays (PAM) -... (1)

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Created: 2011-07-14

Prediction Analysis for Microarrays (PAM) ... (1)

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Created: 2011-07-14

Prediction Analysis for Microarrays (PAM) ... (1)

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Created: 2011-07-14

Backward Elimination via Hilbert-Schmidt I... (1)

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Created: 2011-07-14

Backward Elimination via Hilbert-Schmidt I... (1)

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Created: 2011-07-14

Dense Relevant Attribute Group Selector (D... (1)

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Created: 2011-07-14

Dense Relevant Attribute Group Selector (D... (1)

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Created: 2011-07-14

Consensus Groups Stable Feature Selection ... (1)

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Created: 2011-07-14 | Last updated: 2011-07-14

Consensus Group Stable Feature Selector (C... (1)

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Created: 2011-07-14

Unbalanced (1)

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Created: 2014-04-17

Predicting the fuel consumption of cars (1)

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This process is using features extracted from DBpedia to predict the fuel consumption of cars. It uses operators from the Linked Open Data and the Weka extensions.The process first reads a list of cars with the fuel consumption value. The DBpedia lookup linker is used to link the car names to DBpeida resources. The established links are used to generate additional features, i.e. direct types and categories of each car. Finally, a M5 Rules Operator is used to predict the fuel co...

Created: 2014-05-14 | Last updated: 2014-05-15

The Demo Process in RapidAnalytics Video (1)

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The first demo process used in the new version of RapidAnalytics video on exposing RapidAnalytics Processes as Web Services.

Created: 2014-07-08 | Last updated: 2014-07-08

Acquiring background knowledge on companie... (1)

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This workflow shows how to add background knowledge on companies (e.g., turnover, number of employees, branch) from DBpedia to a dataset.

Created: 2013-09-16

WebCrawl with word filter (1)

WebCrawl with word filter

Created: 2013-09-23

Crawl Web with filter words (1)

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Crawl Web with filter words

Created: 2013-09-30

SentenceSplit (1)

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Created: 2013-11-27

RCOMM 2013 Challenge: 1. Generate input data (1)

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This process generates the input for one of the RCOMM 2013 data mining challenge tasks. For a description, please refer to the solution process.

Created: 2013-09-09 | Last updated: 2013-09-09

RCOMM 2013 Challenge: 2. Solution (Re-infe... (1)

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This process is the solution for one of the RCOMM 2013 data mining challenge tasks which participants had to solve within 10 minutes. The task was this: Given (1) a variant of the Golf data set (found in the //Samples/data folder) where the attribute Outlook is missing, (2) a decision tree model built on the complete Golf data set, and (3) a utility data set containing only the three distinct values of Golf, create an example set based on the incomplete data set from (1) containing all po...

Created: 2013-09-09

Using Linked Open Data from Eurostat as Ba... (2)

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This process shows how an existing data table containing data about countries can be enriched with background knowledge from Eurostat. The example takes tabular data about the alcohol consumption in European countries as input, adds more data about those countries from Eurostat, and finally learns a model based on the enriched data which explains high alcohol consumption.

Created: 2013-09-12 | Last updated: 2013-09-13

image_mining_with_rapidminer_647205 (1)

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Created: 2014-02-07

http://www.myexperiment.org/workflows/1229... (1)

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Created: 2014-02-07

Image mining (1)

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Created: 2014-02-07

image mining (1)

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Created: 2014-02-09